Effect of candidate genes for maternal ability on piglet survival and growth

Author’s Accepted Manuscript Effect of candidate genes for maternal ability on piglet survival and growth E. Jonas, L. Rydhmer

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PII: DOI: Reference:

S1871-1413(17)30359-1 https://doi.org/10.1016/j.livsci.2017.11.018 LIVSCI3356

To appear in: Livestock Science Received date: 8 June 2017 Revised date: 18 October 2017 Accepted date: 21 November 2017 Cite this article as: E. Jonas and L. Rydhmer, Effect of candidate genes for maternal ability on piglet survival and growth, Livestock Science, https://doi.org/10.1016/j.livsci.2017.11.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Effect of candidate genes for maternal ability on piglet survival and growth

E. Jonas1 and L. Rydhmer1 1

Department of Animal Breeding and Genetics, Swedish University of Agricultural

Sciences, Box 7023, SE-750 07 Uppsala, Sweden

Corresponding author: Elisabeth Jonas. E-mail: [email protected].

Short title: Association Study for Sow’s maternal Ability

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ABSTRACT Our study aimed to test if genes related to maternal ability can be used as genetic markers to improve piglet production. We considered polymorphisms in the oxytocin gene and other loci related to metabolic oxytocin levels and maternal behavior. We hypothesized that genetic variants in these genes can be used to select sows with good mothering ability, expressed as the ability to raise many fast-growing piglets. We identified polymorphisms in candidate genes and used additional closely located known polymorphisms to genotype sows and to test the association of the polymorphisms. Nine genes, oxytocin (OXT), oxytocin receptor (OXTR), mesoderm-specific transcript (MEST), paternally expressed gene 3 (Peg3), growth factor receptor-bound substrate 10 (Grb10), FBJ murine osteosarcoma viral oncogene homolog B (FOSB), cluster of differentiation 38 (CD38), neurohypophyseal hormone arginine vasopressin (AVP) and protein kinase C, gamma (PRKCG) were selected for analysis. We identified four novel single nucleotide polymorphisms (SNP), while 23 SNP identified using sequencing were already reported in a public database. Sows were genotyped using SNP identified using sequencing and from a public database, and a total of 30 out of 65 SNP segregated in the population. We tested the association of 20 markers with traits from up to 164 sows, including number and weight of piglets born and weaned and growth rate of piglets until week 5. Polymorphisms in or close to genes FOSB, PRKCG, Grb10, OXTR, and AVP showed significant associations (after Bonferroni correction, P < 0.00256) with mean birth weight, piglets stillborn of total born and relative weight change of the sow during lactation. We identified some effects (P < 0.05) of SNP close to or within OXT, MEST,

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FOSB, AVP and PRKCG on number of piglets dead or stillborn from total or live born. Birth weight and piglet growth were slightly (P < 0.05) influenced by polymorphisms in or close to genes Grb10, Peg3 and PRKCG. Two markers in the regions of genes MEST and Grb10 showed an effect (P < 0.05) on the relative fat and weight change of the sow during lactation, respectively. Most of the associations were either identified in the first or second parity, indicating strong differences between the traits across these early parities. Future studies should investigate the correlation between maternal behavior traits and the traits investigated here and test the effect of the investigated loci on behavior in sows. If these genes are associated with favorable maternal behaviors in pigs and if they are useful indicators of the maternal ability, they could be used to identify sows with high genetic ability to raise many fast growing piglets.

Keywords: association, candidate genes, maternal behavior, oxytocin, piglet survival

Implications The survival of piglets is one important measure of successful pig production and the sow plays a major role for the survival of piglets. Selection of maternal lines focusses on maternal ability as it is relevant for piglet survival. We found some associations between candidate gene markers and maternal ability traits. But since the associations were not consistent from the first to the second parity, the markers seem to be less valuable for breeding.

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Introduction The husbandry and production of livestock is complex and depends on many variables of the animal itself, but also environmental factors. Breeding programs aim to consider these factors by integrating information from relatives kept also in different environments into the prediction of breeding values for the selection of breeding stock. Advanced tools including marker-assisted selection (MAS) and genomic selection (GS) make use of molecular genetic markers. Both methods are especially of interest for an improved selection of traits with low heritability and expensive and labor-intensive recording. The identification of genetic markers or quantitative trait loci (QTL) on the genome may also be useful when considering traits important for animal welfare, such as maternal ability, in the selection. Maternal ability can be described using the sow’s behavior pattern before, during and after farrowing. Behavior has an effect on the production outcome and the maternal behavior in pigs will affect the survival of the piglets (Grandinson, 2005, Hellbruegge et al., 2008, Lundgren et al., 2010). With successes seen when improving the total number of piglets born, the need for more emphasis on the survival of piglets is evident. While the piglet’s vitality and the environment are relevant for the survival, also the mothering ability of the sow contributes. Genes relevant for maternal behavior may show association with the number of piglets weaned. A number of regions on the genome have been identified for behavior in pigs, and some potential candidates have been investigated for maternal behavioral traits (Muráni et al., 2010, Chen et al., 2011). Genes can be identified based on the hormonal impact on reproductive behavior, as 4

summarized in a review in pigs (Algers and Uvnäs-Moberg, 2007). Hormones play a role during the farrowing to weaning period on behaviors related to nest-building, farrowing and lactation (Algers and Uvnäs-Moberg, 2007). Oxytocin is one of the well-known hormones with impact on farrowing and lactation. It is therefore relevant for piglet survival (Nishimori et al., 1996). In a study in human, the effect of an SNP in the oxytocin receptor gene (OXTR) on parenting and social behavior was demonstrated (Michalska et al., 2014). However, it is unknown if this effect can also be shown in pigs and if there is a clear link between behavior, maternal ability and piglet survival. Also other genes have been discussed regarding maternal behavior. The effect of such genes and their receptors might be relevant when investigating the molecular genetic background of maternal behavior. It is furthermore not known if such genes will also have a direct effect on piglet survival. We will study the effect of oxytocin and other candidate genes on maternal behavior in a coming study. In this first study we wanted to investigate effects of oxytocin and other candidate genes on litter size, piglet growth and piglet mortality, i.e. maternal ability. Our aim was to identify polymorphisms in potential candidate genes for maternal ability and to test their segregation in sows from the Swedish Yorkshire population. We chose genes described in the literature as candidates for behavioral traits and genes related to the regulation of oxytocin levels, i.e. genes in the same pathway as oxytocin (OXT) and oxytocin gene receptor (OXTR). Our hypotheses are that genes which play a role in the regulation of oxytocin levels in the blood segregate in the Swedish Yorkshire population and that they can be used to identify sows with good maternal ability.

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Material and Methods All applicable international, national and institutional guidelines for the care and use of animals were followed. The study was approved by the Ethics Committee for Animal Experimentation, Uppsala, Sweden (C12/14). Animals and samples Blood or hair samples were collected from 176 purebred Yorkshire sows, gilts and female piglets at the Swedish Livestock Research Centre Lövsta of the Swedish University of Agricultural Sciences (SLU) at different time points between 2014 and 2015. Samples were taken from purebred Yorkshire sows and gilts on the farm and they were born between March 2011 and January 2015. Blood samples were taken from the external jugular vein and collected in 5 ml EDTA (ethylenediaminetetraacetic acid) tubes, transported to the lab, stored at -20˚C and aliquots of the blood were prepared within a week after sample collection. Between 1 and 3 cryo-tubes were filled with up to 2 ml blood while 320 µl blood was pipetted into a tube for DNA isolation. All samples were thereafter stored in the -80˚C freezer of the SLU. Blood samples could not be taken from 3 sows; hair samples were collected from these sows.

Phenotypes The husbandry set-up at the Swedish Livestock Research Centre Lövsta is according to the Swedish norm, which implements loose housing of sows in individual pens during 6

farrowing and lactation. Each unit in the research farm included 12 farrowing pens. The sows farrowed in batches with approximately 45 (between 11 and 57) sows per batch. Piglets were weaned at 5 weeks of age. The standard phenotypic recording on the research farm includes information on the environment (housing, treatment, batch), the sow (date of birth, pedigree, parity, date of farrowing and weaning) and the piglets (piglet weight and litter size at birth and weaning, sex, birth- and weaning date). Twelve of the animals with blood or hair samples were culled before farrowing. Data from the first parity from 164 sows and from the second parity from 128 of these sows were used to derive traits describing maternal ability: total number of piglets born, dead piglets of total born piglets (from birth to weaning, including stillborn, expressed as a ratio from 0 to 1), dead piglets of live born piglets (from birth to weaning, expressed as a ratio from 0 to 1), stillborn of total born piglets (piglets born as stillborn, expressed as a ratio from 0 to 1), mean birth weight, mean growth rate from birth to five weeks and two relative body measurement traits of the sows (difference of body weight and backfat measured with ultrasound after farrowing and the day of weaning). Information from litters affected by cross-fostering was only included in the association analysis for the traits recorded at farrowing (number piglets born, mean birth weight, piglets stillborn of total born). All traits included in this study showed considerable variation (Table 2).

DNA preparation Blood samples were used for DNA isolation using the instructions for DNA isolation from blood and the DNeasy mini kit (Qiagen) for the QIAsymphony (Qiagen). DNA from hair 7

samples was isolated using a Chelex based protocol using 10-15 hair roots and adding 200 µl 5% Chelex 100 (Biorad) and 10 µl of 10 mg/mL ProteinaseK (Qiagen), incubation at 55˚C for 30 minutes before heating the sample at 96˚C for 5 minutes to inactive the ProteinaseK. DNA concentration and quality was evaluated on a Thermo Scientific™ NanoDrop 8000. DNA was stored in 20˚C before using it for the further analysis of genes using sequencing and for genotyping. DNA was diluted to a concentration of 4 ng/µl for sequencing and 10 ng/μl for genotyping.

Selection of candidate genes To identify candidate genes or regions, we combined information on QTL regions for behavioral and reproductive traits with information on positions of potential candidate genes for maternal or social behavior (Supplementary Figure S1). The choice of genes and fragments within the genes was done based on literature and database search (QTL DB, SNP DB, Ensembl). The main genes of interest, oxytocin (OXT) and oxytocin gene receptor (OXTR) were used for genotyping. Additional genes were identified which have a potential effect or are in the same pathway as OXT and OXTR. Further candidate genes were identified from publications on QTL for behavioral and reproduction traits in pigs (http://www.animalgenome.org/cgi-bin/gbrowse/pig/#search) (Hu et al., 2016) and gene functions in different species as described in OMIM (http://www.omim.org/). Information on structure and position of the genes was extracted from Ensembl (http://www.ensembl.org/) and information on polymorphisms was used to

8

choose the fragments for sequencing (Supplementary Table S1). More information on the selected candidate genes is listed in Supplementary Table S2.

Sequencing Samples for sequencing were selected based on the concentrations, optical density ratios and parentage. The deeper pedigree of the sampled animals was identified using information

from

the

artificial

insemination

station

on

the

website

http://www.qgenetics.se/semin/semingaltar. Sire identifications of each sow were added and clusters of similar sires were created. Samples from between one and five sows were used for sequencing. Sows for sequencing were selected based on the diverse pedigree (no or few sires in common for up to 3 generations). Sequence information was obtained from the Ensembl database for 8 selected genes including FBJ murine osteosarcoma viral oncogene homolog B (FOSB) and paternally expressed gene 3 (Peg3) on chromosome 6, cluster of differentiation 38 (CD38) on chromosome 8, growth factor receptor-bound substrate 10 (Grb10) on chromosome 9, oxytocin gene receptor (OXTR) on chromosome 13, oxytocin (OXT) and neurohypophyseal hormone arginine vasopressin (AVP) on chromosome 17, mesoderm-specific transcript (MEST) on chromosome 18. Protein kinase C, gamma (PRKCG) on chromosome 6 was included during the sequencing process, because it was located between genes FOSB and Peg3. Information on sequences as well as already described mutations and detailed information collected from the SNP database (http://www.ncbi.nlm.nih.gov/SNP/) was summarized. Exons with previously described SNP were chosen and the online program 9

Primer3 http://primer3.ut.ee/ was used to identify primers covering exons and SNP for further

sequencing.

M13

tails

CAGGAAACAGCTATGACC)

were

(fw: added

TGTAAAACGACGGCCAGT to

reverse

and

forward

and

rv:

primers

(Supplementary Table S3), which were ordered from TAG Copenhagen. Primers were diluted to 100 µM and working solutions of a concentration of 10 µM were prepared. Sequencing was performed using the Big Dye sequencing kit (Applied Biosystems). The polymerase chain reaction (PCR) was performed in a total volume of 10.0 μL with 1.5 uL M13-tailed PCR primer mix (0.8 μM each primer), 5.0 μL BigDye® direct PCR Master Mix, 2.5 μL deionized water and 1.0 μL genomic DNA (4 ng/μL) and with the following conditions: 96°C for 5min, followed by 35 cycles at 94°C 30 sec, 62°C 45 sec and 68°C 45 sec. The PCR was finalized with a final extension of 72°C for 2 min. Samples were thereafter briefly spun down before adding the sequencing mix using 2.0 μL BigDye® Direct Sequencing Master Mix, 1.0 μL BigDye® Direct M13 forward or reverse primer to the PCR product. The sequencing reaction was performed by 37°C for 15 min, 80°C for 2 min and 96°C for 1 min, followed by 25 cycles of 96°C for 10 sec, 50°C for 5 sec and 60°C for 4 min. Products were spun down before cleaning using a mix of 45 μL SAM™ Solution and 10 μL XTerminator® Solution (Applied Biosystems). The mix was added to the product from the sequencing reaction and thoroughly mixed by inserting and vortexing. Samples were spun down before starting the sequence analysis on an ABI3500 (Applied Biosystems). Sequence information was thereafter stored and data checked using the software Bioedit http://bioedit.software.informer.com/ . Sequences were

aligned

to

each

other

and

the

10

original

sequence

using

MultiAlign

http://multalin.toulouse.inra.fr/multalin/ . If no polymorphic sites were identified using the samples, alternative primer pairs were tested. New primer pairs were also designed if the sequence reaction failed. All primers are listed in Supplementary Table S3.

Genotyping method The Sequenom iPLEX Gold assay at the MAF genotyping service of the Karolinska Institute in Huddinge, Stockholm http://www.maf.ki.se/service_genotyping.html was used for genotyping of all sows. The service included design, oligo manufacture and pooling, iPLEX assay, spot and fire on MA4, data analysis and delivery of results. Approximately 500 ng DNA per sample were run on three 96-well microtiter plates, using two plates with samples and one plate for positive control samples with known genotypes (from sequencing and previous results). Fourteen negative controls were added to the plates by the service provider. A total of 45 SNP were selected for the analysis out of which 38 markers had been selected from the commercial porcine 60kb SNP array. The SNP were selected based on the design of the array with the aim of covering each of the genes with at least 2 SNP and using as many of the identified SNP as possible. Samples from 27 animals were also genotyped using the commercial 60kb SNP array (http://www.illumina.com/products/porcinesnp60_dna_analysis_kit.html) and were used to test the validity of the assay based on the 38 markers. The genotypes of the markers obtained from the Sequenom iPLEX Gold assay and the commercial 60kb SNP array were compared, only markers with identical results were further included in the association analysis. 11

Statistical analysis Genotype- and allele frequencies were calculated. Markers were tested for HardyWeinberg equilibrium. Markers in the same regions were also tested for linkage disequilibrium. Association analysis with maternal ability of the sows was done using linear models in the SAS software, version 9.3 (SAS Institute Inc. 2011. Base SAS® 9.3 Procedures Guide. Cary). Associations were tested for traits in parity 1 and 2 separately as these traits will likely differ regarding their genetic background. Dependent traits were: total number of piglets born, number of piglets born alive, dead piglets of total born piglets, dead piglets of live born piglets, stillborn of total born piglets, mean birth weight and mean growth rate from birth to five weeks. Two body condition traits of the sows during the trial were used, the relative backfat change and relative weight change during lactation. Linear models were used in the association analysis, including the fixed effect of batch. Total number of piglets born was added to the model as a regression for the traits stillborn of total born, dead piglets of total born piglets and mean birth weight. Number of piglets born alive was added for dead piglets of live born piglets, mean growth rate from birth to five weeks, relative body weight change and relative backfat change of the sow during lactation. Some markers were not used in the association analysis, due to mismatches (one genotype not identified), missing heterozygotes or complete linkage disequilibrium with another marker (see Table 1). The association analysis was performed with the following

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markers:

rs709419487,

rs80828140, 12

rs342540554,

rs80882394,

FOSB_exon, rs81395957, rs81251704, rs323213239, rs333285395, rs81332684, rs81323793,

rs343565479,

rs81218773,

rs81387800,

rs81387820,

rs81387823,

rs81394561, rs322878377, rs80855152. Additionally, a combined marker was used which included information from marker rs700331789 in which missing values were added using information from marker rs80826739, as both markers were in complete linkage disequilibrium for animals with records. A Bonferroni correction (1 − (1 – 0.05)1/n with n = 20) was used to correct for multiple testing. Results were thereafter deemed significant with a P-value < 0.00256 and an effect was assumed for P-values < 0.05. If multiple markers within different genes were significantly associated with the same traits, the interaction of their genotypes was tested. Interaction was tested using linear models in the SAS software, version 9.3 (SAS Institute Inc. 2011. Base SAS® 9.3 Procedures Guide. Cary) as described for single markers. To test the combined effects of two markers, fixed effects of both genotypes as well as the interaction between both genotypes were included.

Results Candidate genes and identified SNP The association study presented here focused on traits influenced by maternal ability, which is closely linked to maternal behavior. We will study maternal behavior and the connection to maternal ability and the traits investigated here in detail in future analyses. Both sequencing and genotyping were successful; we could identify and confirm SNP which segregated in the population. Of the 45 markers, 30 segregated in the population 13

and 20 turned out to be useful for the association analysis. Six of the markers (in 5 genes) were found to be associated with maternal ability traits, while 11 markers (in 7 genes) showed an effect. We identified SNP in seven of the studied candidate genes; FOSB, CD38, Grb10, OXTR, OXT, AVP and MEST. No SNP was identified in the gene Peg3 (Supplementary Table S4) and the gene PRKCG was only included due to the close location with some of the included markers for genes FOSB and Peg3. Genotypes were available from markers close to or within all genes (Table 1). For all genes except FOSB sequence information and a list of polymorphisms with detailed information can be

found

in

the

Ensembl

database

http://www.ensembl.org/Sus_scrofa/Gene/Variation_Gene. Some of the SNP used in our study were chosen based on the information given in the database. When crossvalidating the SNP identified using sequencing with those in the database, most SNP had been reported previously, the SNP in intron 1 of AVP was rs342540554, the three SNP in intron 8 of CD38 were rs81290385, rs709419487 and rs322025329, the SNP in intron 7 of Grb10 were rs323213239 and rs333285395, the two SNP in MEST were rs343565479 and rs81218773, the SNP in exon 3 of OXTR was rs322878377 and the SNP in OXT were rs80897491, rs335086766 (missense mutation), rs80838041, rs80911607, rs700331789, rs80826739, rs339834740, rs340732474, rs80828396 and rs80932027. Four novel SNP were identified, two of which were in FOSB and were used for genotyping (FOSB_exon, FOSB_UTR). One of these SNP in FOSB and one in Grb10 were not used for genotyping as these markers could not be placed on the genotyping array (Supplementary Table S4).

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Genotyping We could identify potential polymorphisms in different fragments using sequencing (details in Supplementary Table S4). Additional markers were chosen from the SNP database in Ensembl and the porcine 60kb SNP array. The figure in Supplementary Figure S1 describes the steps used in the study. The OXT gene was highly polymorph in the studied animals. We identified eight SNP in intron 1, one in exon 2, one in intron 2. Potential SNP were identified at two positions in the Grb10 gene, two positions in the second fragment of CD38 and in intron 9 and exon 10 of the MEST gene. Potential SNP were identified at two positions of the OXTR gene and one position in the AVP gene. However, the quality of the sequences was not good enough to finally determine if these positions showed heterozygote or homozygote genotypes. Sequenced animals showed the other homozygote genotype compared to that suggested for the reference sequence in the Ensembl database for three positions in the CD38 fragment, one position of OXTR, and two positions in two fragments of the FOSB gene. Not all SNP used for the genotyping assay did segregate in the population (Supplementary Table S4). As shown in Supplementary Figure S1, additional SNP markers from the porcine 60kb SNP array and the SNP database on Ensembl were included in the genotyping assay to have a better coverage of the genes and also to be used as controls. A total of 45 markers were part of the genotyping assay. The comparison of the results from the commercial SNP arrays and the MAF analysis of the common SNP showed a good agreement. Samples with unsuccessful alignment with the controls (data from the 15

genome-wide SNP panel) were removed. Marker rs81310044 showed many mismatches and was not included for the further association analysis. Out of the remaining 44 markers, 11 were monomorphic and four showed problems during the calling and these results were omitted. Further seven markers were not included in the association analysis as they were in linkage disequilibrium with another SNP, and one marker was not further used as none of the genotyped animals was heterozygote and the population was not in Hardy-Weinberg equilibrium at the locus. Allele and genotype frequencies of the 30 segregating markers are shown in Table 1. Eleven of these markers were not used in the association analysis. Genotypes from multiple SNP were available for most genes. The three SNP closest to the PRKCG gene showed no strong linkage disequilibrium with each other and all three markers were used for the association analysis. Two SNP in FOSB (FOSB_exon, rs81395941) were in complete linkage disequilibrium but showed recombination with marker rs81395957. Heterozygote genotypes were missing for the SNP FOSB_UTR, therefore only two out of four possible markers were further used. Three segregating SNP in CD38 were in complete linkage disequilibrium and only one marker was used. Three of the SNP (rs323213239, rs333285395 and rs81251704) assigned to Grb10 showed some linkage disequilibrium with each other, but were kept in the analysis. One of the six SNP for Grb10 was excluded due to mismatches. Three SNP used for genotyping at the locus of OXTR were in complete linkage disequilibrium and only one marker was used for the association analysis. Two of the SNP identified in the OXT genes (rs700331789, rs80826739) were in complete linkage disequilibrium;

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and both loci had a relatively high missing frequency. We assigned one additional marker which was a combination between rs700331789 and rs80826739 to have genotypes for all individuals. The additional marker showed no strong linkage disequilibrium with two other SNP. The SNP assigned to AVP were not in high linkage disequilibrium with each other and despite the lack of genotype GG at locus rs80828140, this markers was still in Hardy-Weinberg equilibrium. All markers were therefore used for the association analysis. Two SNP in MEST, rs81472316 and rs81472303, were in complete linkage disequilibrium and in almost complete linkage disequilibrium with marker rs81218773. As these markers derived slightly from the Hardy-Weinberg equilibrium, only one of the markers was included in the association analysis.

Association analysis The association analysis for parity 1 was performed for up to 164 sows and parity 2 for up to 128 of these sows. A number of significant associations (after Bonferroni correction, P < 0.00256) were identified (Table 3) for different markers in AVP, FOSB, Grb10, OXTR and PRKCG. Some effects (P < 0.05) were seen for markers close or within genes AVP, CD38, FOSB, Grb10, MEST, OXT, Peg3, and PRKCG. The most interesting effects were seen for two markers, rs81387800 and rs81387820, close to the PRKCG gene. Both markers were significantly associated with mean birth weight of the piglets in the first parity, and some effect was seen for the same trait in the second parity. The heterozygote genotype of marker rs81387800 had a positive effect, while 17

one homozygote genotype at marker rs81387820 had a positive effect on mean birth weight. Also one marker in FOSB was significantly associated with mean birth weight (Table 3). Two markers close to genes Grb10 and Peg3 showed some effect on the growth rate until week 5 in parity 2 and 1, respectively, however neither reached the significant threshold. A number of suggestive and significant associations were identified for the piglet mortality traits; piglets dead of live born, piglets dead of total born and piglets stillborn of total born. Suggestive effects on the mortality of piglets in both parities was seen for one marker in FOSB with the GG genotype having a positive effect on survival. For none of the other traits, the effects were observed in both parities. Marker rs80828140 in AVP had some effect on the piglets dead and a significant effect on the number of stillborn piglets. While the OXT marker had only a suggestive effect on the number stillborn piglets in the first parity, the OXTR marker was significantly associated with the number of stillborn piglets in the second parity. A significant association was also found for one marker close to Grb10 and the relative weight change of the sow in the second parity. When combining loci which were significantly associated for the same trait, no significant association of the interaction between markers in FOSB and PRKCG genes (rs81395957 and rs81387800 or marker rs81395957 and rs81387820) was observed with mean birth weight during the first parity. Also the interaction term of markers in genes AVP and OXTR (rs80828140 and rs322878377) did not show significant association, however, both markers were significantly associated. In a model with both markers rs81218773 in MEST and rs80828140 in AVP, marker s80828140 did reach

18

significance (P < 0.0018) and marker rs81218773 showed suggestive (P < 0.031) Pvalues in the second parity (results not shown).

Discussion Even though most of the identified SNP were not novel, for most of the studied genes no association studies have been published in pigs and we are not aware of any presented evidence for the function of the selected genes in pigs in the literature. Some of the previously identified QTL in pigs might be linked to the genes, however, there is a lack of studies focusing on the genes themselves. Exported data from the QTL database did not show significant associations of the same markers for the same or related traits (Hu et al., 2016); we therefore compared our results with the regions given in the database. Oxytocin is one of the more well-known hormones related to social interactions. It is also a well investigated hormone which multiple functions on blood pressure, uterus contraction, and milk ejection were already described more than 100 years ago (Oliver and Schafer, 1895, Ott and Scott, 1910, Schafer and Mackenzie, 1911). Oxytocin has an effect on the ability to nurse or more specifically eject milk, it contributes therefore also to the survival of piglets. We identify an effect of the combined OXT marker with number of stillborn piglets (P = 0.0197) in the first parity (Table 3). An evolutionary study in primates found evidence for a correlation between OXT and litter size in the Cebidae family (Vargas-Pinilla et al., 2015). The QTL database lists QTL for number of stillborn and litter size in Large White pigs, which are relatively close to the position of OXT, however the suggested candidate genes do not include OXT (Onteru et al., 2012). 19

Oxytocin has only one single type of receptor, governed by OXTR, which has been shown to be associated with plasma oxytocin levels (Feldman et al., 2012). In our association study, OXTR was significantly associated with number of stillborn piglets (P = 0.0001), but only in the second parity. QTL for number of stillborn was also identified in another study in a region close to OXTR and the marker used in our study (Schneider et al., 2015). Oxytocin levels in the blood are relevant during farrowing, which might affect the number of stillborn piglets. This might be partly confirmed by the findings of Schneider et al. (2015) who did not identify a QTL for litter size in this region, suggesting that a further study of underlying genes will be relevant to understand why only some QTL have pleiotropic effects on litter size and number of stillborn piglets. CD38 was the second most promising candidate gene in our study as it had been described as a relevant gene influencing the plasma level of oxytocin (Jin et al., 2007). While many SNP have been listed in the SNP database, there are, to our knowledge, no association studies of the porcine CD38 gene in pigs or the study of their effect on the regulation of oxytocin levels in the plasma. Only a slight effect (P = 0.0547) of the SNP in CD38 (rs709419487) on piglets born dead of total number of piglets in the first parity was seen in our study. QTL for litter size and total number of piglets born alive are listed in the QTL database close to the marker used in our study, however secreted phosphoprotein 1 (SPP1) is suggested as candidate gene for this QTL (Hernandez et al., 2014). The neurohypophyseal hormone arginine vasopressin (AVP) is synthesized in the same parts of the hypothalamus as oxytocin (Brownstein et al., 1980). A study investigating the HPA axis pathway in pigs showed the association of one of the AVP receptors,

20

AVPR1B, with aggressive behavior in young pigs (Muráni et al., 2010). One marker (rs80828140) investigated in our study was associated with piglet survival, however, one homozygote genotype was not identified in our study; results have therefore to be interpreted carefully. Genotypes on another marker rs80882394 in AVP showed an effect on the number of stillborn piglets in the first parity, a trait for which marker rs80828140 showed significant association. Two studies have identified QTL for litter size and number of stillborn close to the marker used in our study (Onteru et al., 2012, Schneider et al., 2012). The sequence of the porcine FOSB gene was firstly analyzed by Helm et al. (1999). One polymorphism showed a slightly different allele frequency in traditional maternal compared to paternal pig lines in their study, however additional studies were suggested to investigate a possible effect (Helm et al., 1999). The suggestion that FOSB has an effect on maternal traits was derived from findings in mice, which showed that FOSB is an important genetic control mechanism as is leads adaptive neuronal response and is relevant for nurturing in mammals (Brown et al., 1996). In our study one SNP used for the association analysis was significantly associated with mean birth weight, and showed some effect on mortality, with genotype GG being favorable (higher birth weight, lower mortality) for these traits. QTL for litter weight are listed in the QTL database close to the marker used in our study (Schneider et al., 2012). Three imprinted genes, Grb10, MEST and Peg3 were included in our study. The few studies published in pigs so far, have focused on the expression and imprinting pattern (for example in Zhou et al., 2011, Congras et al., 2014). There is currently no report on the effect of Grb10 in sows. However, Grb10 may have an effect

21

on litter size in pigs, because studies in mice showed that an effect on litter size, size of each offspring and growth in mice (Shiura et al., 2009, Charalambous et al., 2010). A study using genome-wide markers identified an association with growth for a haplotype covering the porcine Grb10 gene (Gärke et al., 2014). One marker in our study was significantly associated with the relative weight change of the sow in the second parity, while another marker showed some effect on the growth rate of the piglets in the same parity in our study (Table 3). Many QTL for early body weight are listed in the QTL database (Hu et al., 2016), suggesting the position of a candidate gene in this region. The study of Charalambous et al. (2010) did further suggest that this gene does influence the relation between number of offspring versus size of offspring in mice. However, the results here could not confirm this effect. The gene MEST is only expressed when inherited paternally. We could identify effects of two markers in MEST on stillborn piglets in the first parity and an effect of one marker on the relative backfat change in sows. A study in mice showed that MEST has a function on fat mass in relation to the energy balance (Nikonova et al., 2008). QTL for litter size and piglets born alive were previously identified in this region (Coster et al., 2012). Some effect on the piglet growth rate until week 5 was shown for Peg3. It has been shown that Peg3 is also imprinted in pig (Zhou et al., 2011), however no further study has suggested the effect of this gene on maternal ability, while a QTL for body weight at birth was previously identified (Schneider et al., 2012). The porcine PRKCG gene was included additionally in our study because this gene is located between the two candidate genes Peg3 and FOSB. Interestingly, two of the SNP used for the association study with PRKCG showed

22

significant associations with mean birth weight in the first parity, while an effect was still seen in the second parity. Marker rs81387820 had an effect on the number of dead piglets in the first parity (Table 3). However, the frequency of the unfavorable genotype was very low (Table 1). Because no other report has shown the effect of this gene on growth or body weight, it is possible that the association identified here is derived from a linked gene. Additional studies would be needed to further test the effect of PRKCG. Interesting findings of our study were, additional to the observed associations, that only few of the effects were seen across the first and second parity. We hypothesize that some traits have a distinctly different genetic regulation in the first and later parities. Studies in pigs showed for example that genetic correlations between the first and second parity differed based on traits (Holm et al., 2005). Also in the study of one of the most important markers for litter size, the effects of the marker were not equal across parities (Short et al., 1997). We have not analyzed the genetic correlations between the traits used in our study since the number of sows were low. The effects of the two markers in the PRKCG gene on mean birth weight were seen in both parities, even though the association was only significant in the first parity. This might indicate the genetic link between these two traits. One possible aspect of future studies is the further investigation of the imprinting pattern and improvements for the analysis of imprinted genes. At first thought it may seem simple to record piglet growth and piglet mortality, but the recording is complicated by the management routines. With today’s large litters crossfostering is a common practice in most pig herds. This complicates the genetic

23

evaluation; if a piglet dies three days after farrowing should that influence the breeding value of the biological sow or the foster sow? Knowledge of associations between SNP and piglet growth or piglet mortality based on phenotypes from a reference population (where crossfostering is avoided) could therefore become a valuable complement to the traditional genetic evaluation of pigs. The aim of our study was to identify genes, which are connected to maternal ability in sows, in terms of production of piglets, and change in body condition during the lactation (as an indirect measurement of milk production). We focused on genes relevant for maternal behavior and the oxytocin levels in the blood as our hypothesis is, that maternal behavior influences the maternal ability and thus production of piglets. We identified three novel SNP in the porcine FOSB gene and one novel SNP in the intron of the Grb10 gene. We could show significant associations of SNP in or close to five of the chosen candidate genes with mean birth weight, piglets stillborn of total born and relative weight change of the sow during lactation in Swedish Yorkshire sows. Before farrowing all gilts from the same litter have the same estimated breeding values. SNP associated with piglet growth and piglet survival could be used to select among young gilts within litter. Thus the surplus gilts with lower genetic ability can be raised for slaughter instead of replacement. This is the first study investigating the association of genes related to oxytocin and maternal ability with piglet survival and growth traits in pigs. It will be interesting to further establish the possible impact of the maternal behavior on piglet survival and growth and to test if OXT and OXTR are associated with behavioral traits. Genes typically described as being associated with maternal behavior such as AVP showed

24

association with the number of stillborn piglets. We are planning to use behavioral observations from the sows in this study to further test the association of the candidate genes and the impact of maternal behavior on piglet survival and growth.

Acknowledgements This study was funded by the Swedish Research Council Formas. The authors thank Ulla Schmidt, Elin Eriksson and all staff at the Swedish Livestock Research Centre Lövsta for invaluable help with blood sampling, data collection and recording of many additional traits.

Conflict of Interest statement No conflicts of interest exists.

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Methylation Patterns and Highlights an Altered Methylation at the GNAS Locus in Infertile Boars. Biology of Reproduction 91, 137, 131-114. Coster A, Madsen O, Heuven HC, Dibbits B, Groenen MA, van Arendonk JA, Bovenhuis H 2012. The imprinted gene DIO3 is a candidate gene for litter size in pigs. PLoS One 7(2):e31825. Feldman R, Zagoory-Sharon O, Weisman O, Schneiderman I, Gordon I, Maoz R, Shalev I and Ebstein RP 2012. Sensitive Parenting Is Associated with Plasma Oxytocin and Polymorphisms in the OXTR and CD38 Genes. Biological Psychiatry 72, 175-181. Gärke C, Ytournel F, Sharifi AR, Pimentel ECG, Ludwig A and Simianer H 2014. Footprints of recent selection and variability in breed composition in the Göttingen Minipig genome. Animal Genetics 45, 381-391. Grandinson K 2005. Genetic background of maternal behaviour and its relation to offspring survival. Livestock Production Science 93, 43-50. Hellbruegge B, Toelle KH, Bennewitz J, Henze C, Presuhn U and Krieter J 2008. Genetic aspects regarding piglet losses and the maternal behaviour of sows. Part 1. Genetic analysis of piglet mortality and fertility traits in pigs. Animal 2, 1273-1280. Helm JM, Hu Z and Rothschild MF 1999. Rapid communication: Mapping and genetic analysis of porcine FOSB. Journal of Animal Science 77, 2578-2579. Hernandez SC, Finlayson HA, Ashworth CJ, Haley CS, Archibald AL 2014. A genome-wide linkage analysis for reproductive traits in F2 Large White × Meishan cross gilts. Animal Genetics 45(2):191-7. Holm B, Bakken M, Vangen O and Rekaya R 2005. Genetic analysis of age at first service, return rate, litter size, and weaning-to-first service interval of gilts and sows. Journal of Animal Science 83, 41-48. Hu Z-L, Park CA and Reecy JM 2016. Developmental progress and current status of the Animal QTLdb. Nucleic Acids Research 44, D827-D833. Jin D, Liu HX, Hirai H, Torashima T, Nagai T, Lopatina O, Shnayder NA, Yamada K, Noda M, Seike T, Fujita K, Takasawa S, Yokoyama S, Koizumi K, Shiraishi Y, Tanaka S, Hashii M, Yoshihara T, Higashida K, Islam MS, Yamada N, Hayashi K, Noguchi N, Kato I, Okamoto H, Matsushima A, Salmina A, Munesue T, Shimizu N, Mochida S, Asano M and Higashida H 2007. CD38 is critical for social behaviour by regulating oxytocin secretion. Nature 446, 41-45. Lundgren H, Canario L, Grandinson K, Lundeheim N, Zumbach B, Vangen O and Rydhmer L 2010. Genetic analysis of reproductive performance in Landrace sows and its correlation to piglet growth. Livestock Science 128, 173-178. Michalska KJ, Decety J, Liu C, Chen Q, Martz ME, Jacob S, Hipwell A, Lee SS, ChronisTuscano A, Waldman ID and Lahey BB 2014. Genetic Imaging of the Association of Oxytocin Receptor Gene (OXTR) Polymorphisms with Positive Maternal Parenting. Frontiers in Behavioral Neuroscience 8. Muráni E, Ponsuksili S, D'Eath RB, Turner SP, Kurt E, Evans G, Thölking L, Klont R, Foury A, Mormède P and Wimmers K 2010. Association of HPA axis-related genetic variation with stress reactivity and aggressive behaviour in pigs. BMC Genetics 11, 1-11. Nikonova L, Koza RA, Mendoza T, Chao P-M, Curley JP and Kozak LP 2008. Mesodermspecific transcript is associated with fat mass expansion in response to a positive energy balance. The FASEB Journal 22, 3925-3937. Nishimori K, Young LJ, Guo Q, Wang Z, Insel TR and Matzuk MM 1996. Oxytocin is required for nursing but is not essential for parturition or reproductive behavior. Proceedings of the National Academy of Sciences of the United States of America 93, 11699-11704.

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Oliver G and Schafer EA 1895. On the Physiological Action of Extracts of Pituitary Body and certain other Glandular Organs: Preliminary Communication. The Journal of physiology 18, 277279. Onteru SK, Fan B, Du ZQ, Garrick DJ, Stalder KJ, Rothschild MF 2012. A whole-genome association study for pig reproductive traits. Animal Genetics 43(1):18-26. Ott I and Scott JC 1910. The action of infundibulum upon mammary secretion. Proceedings of the Society for Experimental Biology and Medicine 8, 48-49. SAS Institute Inc. 2011. Base SAS® 9.3 Procedures Guide. Cary NSII Base SAS® 9.3 Procedures Guide. Schafer EA and Mackenzie K 1911. The Action of Animal Extracts on Milk Secretion. Proceedings of the Royal Society of London. Series B 84, 16-22. Schneider JF, Miles JR, Brown-Brandl TM, Nienaber JA, Rohrer GA, Vallet JL 2015. Genomewide association analysis for average birth interval and stillbirth in swine. Journal of Animal Sciences 93(2):529-40. Schneider JF, Rempel LA, Snelling WM, Wiedmann RT, Nonneman DJ, Rohrer GA 2012. Genome-wide association study of swine farrowing traits. Part II: Bayesian analysis of marker data. Journal of Animal Sciences 90(10):3360-7. Shiura H, Nakamura K, Hikichi T, Hino T, Oda K, Suzuki-Migishima R, Kohda T, Kaneko-ishino T and Ishino F 2009. Paternal deletion of Meg1/Grb10 DMR causes maternalization of the Meg1/Grb10 cluster in mouse proximal chromosome 11 leading to severe pre- and postnatal growth retardation. Human Molecular Genetics 18. Short TH, Rothschild MF, Southwood OI, McLaren DG, de Vries A, van der Steen H, Eckardt GR, Tuggle CK, Helm J, Vaske DA, Mileham AJ and Plastow GS 1997. Effect of the estrogen receptor locus on reproduction and production traits in four commercial pig lines. Journal of Animal Science 75, 3138-3142. Vargas-Pinilla P, Paixão-Côrtes VR, Paré P, Tovo-Rodrigues L, Vieira CMdAG, Xavier A, Comas D, Pissinatti A, Sinigaglia M, Rigo MM, Vieira GF, Lucion AB, Salzano FM and Bortolini MC 2015. Evolutionary pattern in the OXT-OXTR system in primates: Coevolution and positive selection footprints. Proceedings of the National Academy of Sciences 112, 88-93. Zhou QY, Li CC, Huo JH and Zhao SH 2011. Expression and genomic imprinting of DCN, PON2 and PEG3 genes in porcine placenta. Animal Reproduction Science 123, 70-74.

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Tables Table 1 List of SNP used for genotyping the Yorkshire sows Allele frequency2

Genotype frequency2

A1

A2

A1A1

A1A2 A2A2

0.03

0.14

0.86

0.14

0.00

0.86

159

G

0.01

0.35

0.65

0.14

0.42

0.44

1.03

A

G

0.01

0.35

0.65

0.14

0.42

0.44

1.03

A

G

0.01

0.37

0.63

0.14

0.46

0.40

0.00

rs81387800

C

T

0.03

0.63

0.37

0.40

0.45

0.15

0.34

rs81387820

C

T

0.02

0.82

0.18

0.69

0.26

0.05

2.49

rs81387823

C

T

0.01

0.37

0.63

0.17

0.41

0.43

2.59

A

G

0.01

0.37

0.63

0.17

0.40

0.43

2.87

rs3220253295

C

T

0.01

0.74

0.26

0.55

0.38

0.07

0.00

rs709419487

C

T

0.01

0.26

0.74

0.07

0.38

0.55

0.00

rs812903855

C

T

0.01

0.74

0.26

0.56

0.38

0.07

0.02

rs323213239

C

T

0.01

0.45

0.55

0.21

0.48

0.31

0.12

rs333285395

C

G

0.04

0.63

0.37

0.40

0.47

0.13

0.00

rs813100444

C

T

0.01

0.82

0.18

0.82

0.00

0.18

162

rs81251704

A

G

0.01

0.65

0.35

0.39

0.52

0.09

3.18

rs81323793

A

G

0.01

0.48

0.52

0.22

0.53

0.25

0.78

rs81332684

A

G

0.01

0.49

0.51

0.24

0.51

0.25

0.03

rs812340825

C

T

0.01

0.87

0.13

0.75

0.23

0.02

0.01

rs813308645

A

G

0.03

0.87

0.13

0.76

0.22

0.02

0.06

Marker1

Allele A1

Allele A2

frequency missing

FOSB_UTR6

C

T

rs813959415

A

FOSB_exon rs81395957

HWE3

FOSB

PRKCG

Peg3 rs81394561 CD38

Grb10

OXTR

28

rs322878377

A

G

0.01

0.13

0.87

0.02

0.23

0.75

0.01

rs808267395

A

G

0.12

0.52

0.48

0.28

0.49

0.23

0.05

rs7003317895 rs700331789 + rs80826739 rs80855152

A

G

0.04

0.54

0.46

0.28

0.51

0.21

0.05

A

G

0.00

0.52

0.48

0.27

0.50

0.23

0.01

G

T

0.12

0.25

0.75

0.08

0.35

0.57

0.64

rs342540554

C

T

0.01

0.80

0.20

0.61

0.36

0.02

1.79

rs80828140

G

T

0.01

0.07

0.93

0.00

0.14

0.86

0.95

rs80882394

C

T

0.01

0.48

0.52

0.22

0.50

0.27

0.01

rs343565479

C

T

0.03

0.54

0.46

0.30

0.50

0.21

0.00

rs81218773

C

T

0.01

0.60

0.40

0.31

0.56

0.12

4.47

rs814723165

C

T

0.01

0.40

0.60

0.12

0.56

0.32

4.18

rs814723035

C

T

0.01

0.40

0.60

0.12

0.56

0.31

4.47

OXT

AVP

MEST

Shown are the marker name, the gene closest to the marker, the nucleotide pair, the frequency of animals with no genotypes, and the allele and genotype frequencies in the 158 animals used in the study (excluding missing genotypes) 1 Abbreviations of genes: FBJ murine osteosarcoma viral oncogene homolog B (FOSB), protein kinase C, gamma (PRKCG), paternally expressed gene 3 (Peg3) and cluster of differentiation 38 (CD38), growth factor receptor-bound substrate 10 (Grb10), oxytocin gene receptor (OXTR), oxytocin gene (OXT), neurohypophyseal hormone arginine vasopressin (AVP), mesodermspecific transcript (MEST) 2 Frequencies were calculated excluding the missing alleles 3 Hardy-Weinberg equilibrium: Values above 3.85: population not in Hardy-Weinberg equilibrium at this marker 4 Marker was not included in the final analysis due to many mismatches 5 Marker was not included in the final analysis due to complete linkage disequilibrium with another SNP 6 Marker was not included in the final analysis due to missing heterozygotes

29

Table 2 Descriptive statistics of traits based on all litters used in the study, information separated by the first and second parity Trait

Parity N Mean SD Min Max 1 164 13.23 2.91 3 19 Number piglets born/ litter 2 128 13.95 3.82 1 21 1 164 12.41 2.88 2 19 Number piglets born alive/ litter 2 128 12.97 3.68 1 21 1 158 0.25 0.18 0.00 0.75 Piglets dead of total born 2 112 0.25 0.18 0.00 0.74 1 164 0.06 0.09 0 0.56 Piglets stillborn of total born 2 128 0.07 0.09 0 0.5 1 158 0.20 0.17 0.00 0.75 Piglets dead of live born 2 112 0.19 0.17 0.00 0.70 1 164 1.38 0.22 0.88 1.97 Mean birth weight, kg 2 128 1.56 0.26 1.03 2.26 1 157 338 51 129 510 Mean growth rate birth to 5 weeks, g/d 2 112 361 58 26 503 1 123 -0.25 0.17 -0.57 0.25 Relative backfat change, 1 sow 2 109 -0.23 0.19 -0.59 0.27 1 123 -0.06 0.07 -0.31 0.17 Relative weight change, sow1 2 109 -0.06 0.05 -0.21 0.04 Shown are the number of observations (N), standard deviation (SD), minimum (min) and maximum (max) 1 Negative value indicates that the body weight or backfat decreased during lactation

30

Table 3 Results of the association study for sows’ maternal ability Marker1/ Trait rs81395957 (FOSB) Piglets dead of total born Piglets dead of total born Piglets dead of live born Piglets dead of live born Mean birth weight, kg rs81387820 (PRKCG) Piglets dead of total born Piglets dead of live born Mean birth weight, kg Mean birth weight, kg rs81387800 (PRKCG) Mean birth weight, kg Mean birth weight, kg rs81394561 (Peg3) Mean growth rate birth to 5 weeks, g/d rs709419487 (CD38) Piglets dead of total born rs81332684 (Grb10) Mean growth rate birth to 5 weeks, g/d rs333285395 (Grb10) Relative weight change, sow rs81323793 (Grb10) Relative weight change, sow rs322878377 (OXTR) Piglets stillborn of total born rs700331789 + rs80826739 (OXT) Piglets stillborn of total born rs80828140 (AVP) Piglets dead of total born Piglets stillborn of total born Piglets dead of live born rs80882394 (AVP) Piglets stillborn of total born rs81218773 (MEST) Piglets dead of total born

Parity P-value

N

A1A1

A1A2 A2A2

1 2 1 2 1

0.0421* 0.0526 0.03* 0.0461* 0.0007**

157 111 157 111 163

0.31 0.31 0.27 0.26 1.26

0.25 0.28 0.20 0.21 1.41

0.20 0.20 0.16 0.15 1.44

1 1 1 2

0.0406* 0.0272* 0.0025** 0.0323*

155 155 161 126

0.23 0.18 1.42 1.55

0.24 0.19 1.39 1.53

0.41 0.36 1.17 1.34

1 2

0.0004** 160 0.0264* 125

1.41 1.52

1.43 1.58

1.25 1.42

1

0.0497* 155

321

338

348

1

0.0547

157

0.14

0.28

0.22

2

0.0089* 111

376

337

365

2

0.0201* 107

-0.06

-0.07 -0.03

2

0.0012** 107

-0.03

-0.07 -0.08

2

0.0001** 127

0.54

0.08

0.06

1

0.0197* 164

0.03

0.07

0.05

2 2 2

0.0037* 111 0.0021** 127 0.0142* 111

0.37 0.14 0.29

0.23 0.06 0.18

1

0.0187* 162

0.05

0.07

0.03

2

0.0482* 111

0.20

0.24

0.35

31

rs343565479 (MEST) Piglets stillborn of total born 1 0.0266* 160 0.03 0.08 0.04 rs81218773 (MEST) Piglets stillborn of total born 1 0.0412* 163 0.03 0.07 0.05 Relative backfat change, sow 1 0.0446* 123 -0.21 -0.26 -0.14 Shown are the studied traits, P-values and significance levels (* suggestive P < 0.05, **significant after Bonferroni correction P < 0.00256) for the genetic markers, and least square means for the different genotypes (A1A1, A1A2 and A2A2, see Table 2 for nucleotide pair) 1 Abbreviations of genes: FBJ murine osteosarcoma viral oncogene homolog B (FOSB), protein kinase C, gamma (PRKCG), paternally expressed gene 3 (Peg3), cluster of differentiation 38 (CD38), growth factor receptor-bound substrate 10 (Grb10), oxytocin gene receptor (OXTR), oxytocin (OXT), neurohypophyseal hormone arginine vasopressin (AVP), mesoderm-specific transcript (MEST).

Highlights 

   

First step towards the analysis of the relationship between good mothering ability and the ability to raise many fast-growing piglets based on molecular genetic study Association study for nine genes related to behavior Significant association identified for five genes with mean birth weight, piglets stillborn of total born and relative weight change of the sow Oxytocin gene and oxytocin gene receptor have an effect on piglets stillborn of total born Suggested that genetic markers have no effect across the first and second parity.

32